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Update train.py

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This commit is contained in:
Tianqi Chen 2014-05-16 20:16:10 -07:00
parent 8e5e3340a2
commit cfd6c9e3b7
1 changed files with 6 additions and 2 deletions
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8
demo/multi_classification/train.py
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@ -22,8 +22,7 @@ xg_train = xgb.DMatrix( train_X, label=train_Y)
xg_test = xgb.DMatrix(test_X, label=test_Y) xg_test = xgb.DMatrix(test_X, label=test_Y)
# setup parameters for xgboost # setup parameters for xgboost
param = {} param = {}
# use logistic regression loss, use raw prediction before logistic transformation # use softmax multi-class classification
# since we only need the rank
param['objective'] = 'multi:softmax' param['objective'] = 'multi:softmax'
# scale weight of positive examples # scale weight of positive examples
param['bst:eta'] = 0.1 param['bst:eta'] = 0.1
@ -35,4 +34,9 @@ param['num_class'] = 6
watchlist = [ (xg_train,'train'), (xg_test, 'test') ] watchlist = [ (xg_train,'train'), (xg_test, 'test') ]
num_round = 5 num_round = 5
bst = xgb.train(param, xg_train, num_round, watchlist ); bst = xgb.train(param, xg_train, num_round, watchlist );
# get prediction
pred = bst.predict( xg_test );
print 'error=%f' % sum(int(pred[i]) != test_Y[i] for i in len(test_Y)) / float(len(test_Y))